Healthcare Transformation

Blockchain interoperability needs time and momentum to mature toward a wider scale of adoption and to truly impact healthcare. However, the future is now and the market clearly reflects much buzz around the technology and its potential for hard-coding change. Blockchain interoperability could pave the way toward forming a next-generation vehicle for data exchange that contributes to digital transformation in provider organizations through its network effect.

IDC Health Insights Perspective: Blockchain in Health IT Interoperability explores the potential for blockchain in health IT interoperability as an emerging healthcare provider vertical use case. As healthcare begins to explore the application of blockchain technology, it still faces an entrenched challenge to bridge disparate systems through interoperability and health information exchange (HIE). Ever-expanding data reserves in variety, velocity, and volume and paradoxically in silos…

As IDC Health Insights predicted in August 2017 (Digital Transformation and New Economics Highlight Payer Disintermediation), we anticipated more deals and vertical integration. CVS announced its intention to buy Aetna.in December, and this week Amazon, Berkshire Hathaway, and JPMorgan Chase announced a partnership to cut health-care costs and improve services for their U.S. employees, with the aim of improving employee satisfaction and reducing costs.

As conjectures fly regarding the potential disruption the proposed acquisition of Aetna by CVS will have on the healthcare industry, IDC Health Insights will confine its comments to the technology implications, in particular access to and sharing of data within the existing healthcare ecosystem. This blog will also consider the implications of the acquisition creating a local care delivery presence for Aetna with the intent to increase consumer value.

Will the proposed acquisition of Aetna by CVS create a profound disruption to the healthcare industry seems to be the question of the day. The conjectures are far a field and as varied as the number of authors. The healthcare industry has witnessed consolidation for at least a decade and while the Aetna proposed acquisition is the largest it will likely follow similar patterns of those that went before. First and fore-most change will not occur quickly as regulatory and corporate hurdles exist…

The day-to-day management of fee-for-value (FFV) transactions is virtually 100% manual, with critical calculation of value-based payments performed by spreadsheet or custom programming in SAS or SQL. As the number and breadth of value-based relationships grow, the industry's administrative burden worsens. Important FFV calculations and financial settlements are months delayed, lacking transparency and accuracy. The ability of payers to launch new value-centric benefit products is hobbled by…

Healthcare presents a unique case for DX because of its complex and challenging nature. Nowhere is this precedent more applicable than for the United States, where regulatory frameworks (e.g., MACRA), incentive programs (e.g., QPP, MIPS, and APMs), professional and community advocacy, and consumer-driven market forces are shifting healthcare priorities. These factors are driving the need for data-driven decisions and consumer engagement to recalibrate care from the mere fulfillment of fee for service and driving volume to the realization of pay for performance and driving value. U.S. healthcare organizations have much to gain by embracing DX on their journey toward value-based goals and responding to future challenges.

Digital technologies are changing organizations far and wide. IDC forecasts worldwide spending on digital transformation (DX) across all industries to expand at a CAGR of 17.9% through 2021 to more than $2.1 trillion. This is creating a global landscape with new technologies, players, ecosystems, and ways of doing business for organizations of every size, industry, and context. However, healthcare has traditionally, and rather admittedly, lagged behind other industries in the adoption of…

We hear everyday of the challenges facing provider organizations as they straddle the shifting reimbursement landscape. Well grounded in the fee for service reimbursement models, providers struggle to balance their volume-based operating models with the growth of value-based models. In the former model, if a procedure is done it is reimbursed, regardless of the outcome. The latter it is the outcome that is rewarded and for which at least partial payment is attributed. Adding additional financial risk is the requirement for providers to invest in technology and people to deliver the results required of the contract while not understanding what an expected return on investment might be. We have long understood that improved quality eventually translates into savings, but what are providers to do in the short term to keep the lights on?

For years I have searched and not found financial modeling tools to help healthcare providers evaluate the financial viability of the terms of at-risk contracts and understand the financial impact of the dual reimbursement models. As the level of risk rises in provider organization I worry that too many organizations are blindly accepting clinical and financial risk without the tools to develop a strategy to manage that risk. There are two pieces of the equation that need to be addressed. The…

Czech General University Hospital is already using cloud technology and machine learning, as well as deploying chatbots. In a typically conservative industry, such an example of digital transformation is a true anomaly. We sat down with the hospital's CIO Vlastimil Cerny to discuss why and how this change took place. This is the first in our series of interviews with leaders of digital transformation initiatives in CEE. The series is aimed at informing readers about successful transformations,…

Patient engagement is a journey that offers an advantageous paradigm for healthcare digital transformation strategies. Conceptually, it builds on an appreciation of the changing role for patients (and their families) in modern day healthcare and the importance of generating efforts that organize towards engaging them as care-seeking consumers. In application, patient engagement and its underlying technologies can transform care-seeking consumers into new patients and empower existing patients to become more involved in their health and care. With time this journey can culminate into a digital transformation end state, which combines a mix of patient engagement technologies that collectively achieve an inimitable, competitive, and highly necessary advantage for the provider organization that is conducive to success.

IDC Health Insights recently published two DecisionScape reports on patient engagement, IDC TechScape: U.S. Healthcare Provider Patient Engagement Technologies, 2017 and IDC PlanScape: Patient Engagement for Digital Transformation. The TechScape supplies insights into the risks and business impact of patient engagement technologies to allow provider organizations to better match the technologies to their relative appetite for risk and make informed decisions regarding them. The PlanScape provides a decision-making tool to justify strategic investments and opportunities for providers implementing patient engagement technologies as part of digital transformation strategies. These two reports work in tandem, as the TechScape offers a visual representation of patient engagement technology adoption based on their assessment and characterization into transformational, incremental, or opportunistic technologies; while the PlanScape outlines the who, what, why, and how of patient engagement for digital transformation.

Patient engagement technologies can help digitally transform healthcare provider organizations to impact on outcomes, value, and consumerism along the continuum of care. As U.S. healthcare shifts to a more value-based and consumer driven model, providers must adapt to the opportunities (and challenges) of the changing landscape through digital transformation. This reality implicates the importance of expediency in provider endeavors to adopt patient engagement technologies to reach value-based…

IDC Health Insights recently published IDC Survey: Provider Investment Plans for Robotics. This survey presents key findings from the healthcare section of IDC’s 2017 Worldwide Robotics Survey. The goal of the survey was to assess current and future robotics adoption patterns, use cases, and application areas, as well as investment trends for robots and drones. The results focused on robotics adoption, use cases, and investment plans by U.S. healthcare providers at hospitals with 200+ beds. While the adoption of drones for healthcare utilization is minimal and expected to remain so in the near future, the use of robots in healthcare will yield increased investment in the coming years.

Robotics leapt to the forefront of organizational priorities for technology investments at U.S. hospitals in 2017, followed by cloud and mobility. Hospitals are actively investing into purchasing and deploying robots as a main technology priority, this is being driven by a need to impact the value chain of their business operations.

Robots show a far greater likelihood for deployment in the next one to three years (than drones) and are currently being used to different extents at hospitals,…

Embedding machine learning in healthcare is slowly moving into mainstream. Still a very noisy market with what seems like 100s of start-ups. Through the noise emerges vendors that at early stage are applying machine learning to solve some of healthcare's most pressing problems. The use case getting the most traction in the market is the application of machine learning to predictive analytics, particularly to identify patient's with clinical and financial risk. Other applications include automating medical record review to validate Hierarchical Condition Coding (HCC), a process that was manual and caused friction between payers and providers, improving patient engagement for care management through mobile technology and identifying variation in clinical practice and recommending best practices.

The initial introduction of machine learning in healthcare was slow to catch on as healthcare organizations struggled to gain the expertise to understand the relevant data, create algorithms, and consume the applications across their organizations. Embedding machine learning in applications is allowing the democratization of the technology. IDC recently published a report, IDC Innovators: Machine Learning in Healthcare, 2017 (#US42826817) that profiled four vendors: Apixio, Ayasdi,…